Pseudobulk - Macrophages - by Sample
suppressMessages(library(ComplexHeatmap))
suppressMessages(library(dplyr))
suppressMessages(library(viridis))
suppressMessages(library(SingleR))
suppressMessages(library(pheatmap))
suppressMessages(library(edgeR))
suppressMessages(library(limma))
suppressMessages(library(magrittr))
suppressMessages(library(ggbiplot))
suppressMessages(library(Seurat))sce <- readRDS("/mnt/nmorais-nfs/marta/pA_karine/r_session/e12h-preliminary-analysis/macrophage_subset/data/sce-macrophages.rds")srt <- readRDS("/mnt/nmorais-nfs/marta/pA_karine/r_session/e12h-preliminary-analysis/macrophage_subset/data/srt-macrophages-sorting.rds")hvgs <- VariableFeatures(srt)Aggregate
matrix <- t(assays(sce)$counts)## Loading required package: SingleCellExperiment##
## Attaching package: 'SingleCellExperiment'## The following object is masked from 'package:edgeR':
##
## cpmmatrix <- matrix[,hvgs]
matrix <- as.data.frame(matrix)
matrix <- cbind(Sample = sce$sample, matrix)
rownames(matrix) <- NULL
matrix <- aggregate(matrix[,-1], list(matrix$Sample), sum)matrixmatrix2 <- matrix[,-1]
colnames(matrix2) <- NULL
matrix2 <- t(as.matrix(matrix2))
colnames(matrix2) <- matrix$Group.1
rownames(matrix2) <- hvgs
matrix2[1:5,1:5]## MCT1 MCT3 NT1 S1-NT S2-MCT
## Cxcl2 133031 134495 33205 42454 120361
## Lyz2 72297 58374 297178 162038 308028
## Ccl5 529255 526526 26671 4110 1540
## Cd74 242660 260686 357167 49946 97986
## H2-Aa 132292 136591 150824 21082 36725dim(matrix2)## [1] 3000 12table(rowSums(matrix2) > 0)##
## TRUE
## 3000matrix2 <- DGEList(counts = data.matrix(matrix2))
matrix2 <- calcNormFactors(matrix2)str(matrix2)## Formal class 'DGEList' [package "edgeR"] with 1 slot
## ..@ .Data:List of 2
## .. ..$ : num [1:3000, 1:12] 133031 72297 529255 242660 132292 ...
## .. .. ..- attr(*, "dimnames")=List of 2
## .. .. .. ..$ : chr [1:3000] "Cxcl2" "Lyz2" "Ccl5" "Cd74" ...
## .. .. .. ..$ : chr [1:12] "MCT1" "MCT3" "NT1" "S1-NT" ...
## .. ..$ :'data.frame': 12 obs. of 3 variables:
## .. .. ..$ group : Factor w/ 1 level "1": 1 1 1 1 1 1 1 1 1 1 ...
## .. .. ..$ lib.size : num [1:12] 17201858 17268367 13506251 3162524 5862745 ...
## .. .. ..$ norm.factors: num [1:12] 1.241 1.282 1.277 0.885 0.869 ...
## ..$ names: chr [1:2] "counts" "samples"condition = as.factor(c("MCT-12h", "MCT-12h", "NT", "NT","MCT-72h", "PT", "NT","MCT-72h","PT","PT","NT","MCT-72h"))sample <- c("MCT1","MCT3","NT1", "Sample 1", "Sample 2", "Sample 3",
"Sample 4", "Sample 5", "Sample 6", "Sample 7", "Sample 8", "Sample 9")sorting <- c("Day4", "Day4", "Day4",
"Day1", "Day1",
"Day2", "Day2", "Day2",
"Day3", "Day3", "Day3", "Day3")Without normalization:
pca_wn <- prcomp(t(matrix2$counts), center = TRUE,scale. = FALSE)
ggbiplot(pca_wn, labels.size = 5, var.axes = FALSE,
choices = c(1,2),
groups = condition,
ellipse = TRUE) +
scale_color_manual(values=c("purple","green3" , "cornflowerblue", "coral1" )) +
theme(text = element_text(size=17)) 
ggbiplot(pca_wn, labels.size = 5, var.axes = FALSE,
choices = c(2,3),
groups = condition,
ellipse = TRUE) +
scale_color_manual(values=c("purple","green3" , "cornflowerblue", "coral1" )) +
theme(text = element_text(size=17)) 
ggbiplot(pca_wn, labels.size = 5, var.axes = FALSE,
choices = c(1,2),
groups = sorting,
ellipse = TRUE) +
scale_color_manual(values=c("cyan","blue","orange", "red")) +
theme(text = element_text(size=17)) 
ggbiplot(pca_wn, labels.size = 5, var.axes = FALSE,
choices = c(2,3),
groups = sorting,
ellipse = TRUE) +
scale_color_manual(values=c("cyan","blue","orange", "red")) +
theme(text = element_text(size=17)) 
plotMDS(matrix2, method="bcv", col = c("purple", "purple", "dodgerblue",
"dodgerblue","green3", "coral1",
"dodgerblue", "green3", "coral1",
"coral1", "dodgerblue", "green3"))## Note: the bcv method is now scheduled to be removed in a future release of edgeR.
plotMDS(matrix2, method="bcv", col = c(rep("red", 3),
rep("slateblue4",2),
rep( "darkgoldenrod1", 3),
rep("turquoise1",4)))## Note: the bcv method is now scheduled to be removed in a future release of edgeR.
design <- model.matrix(~ 0 + condition + sorting)
design## conditionMCT-12h conditionMCT-72h conditionNT conditionPT sortingDay2
## 1 1 0 0 0 0
## 2 1 0 0 0 0
## 3 0 0 1 0 0
## 4 0 0 1 0 0
## 5 0 1 0 0 0
## 6 0 0 0 1 1
## 7 0 0 1 0 1
## 8 0 1 0 0 1
## 9 0 0 0 1 0
## 10 0 0 0 1 0
## 11 0 0 1 0 0
## 12 0 1 0 0 0
## sortingDay3 sortingDay4
## 1 0 1
## 2 0 1
## 3 0 1
## 4 0 0
## 5 0 0
## 6 0 0
## 7 0 0
## 8 0 0
## 9 1 0
## 10 1 0
## 11 1 0
## 12 1 0
## attr(,"assign")
## [1] 1 1 1 1 2 2 2
## attr(,"contrasts")
## attr(,"contrasts")$condition
## [1] "contr.treatment"
##
## attr(,"contrasts")$sorting
## [1] "contr.treatment"colnames(design) <- c("MCT-12h", "MCT-72h", "NT", "PT", "Day2", "Day3", "Day4")
design## MCT-12h MCT-72h NT PT Day2 Day3 Day4
## 1 1 0 0 0 0 0 1
## 2 1 0 0 0 0 0 1
## 3 0 0 1 0 0 0 1
## 4 0 0 1 0 0 0 0
## 5 0 1 0 0 0 0 0
## 6 0 0 0 1 1 0 0
## 7 0 0 1 0 1 0 0
## 8 0 1 0 0 1 0 0
## 9 0 0 0 1 0 1 0
## 10 0 0 0 1 0 1 0
## 11 0 0 1 0 0 1 0
## 12 0 1 0 0 0 1 0
## attr(,"assign")
## [1] 1 1 1 1 2 2 2
## attr(,"contrasts")
## attr(,"contrasts")$condition
## [1] "contr.treatment"
##
## attr(,"contrasts")$sorting
## [1] "contr.treatment"v <- voom(matrix2, design, plot=TRUE)
v$E[1:5,1:5]## MCT1 MCT3 NT1 S1-NT S2-MCT
## Cxcl2 12.60505 12.56827 10.91139 13.88836 14.527863
## Lyz2 11.72530 11.36412 14.07322 15.82071 15.883553
## Ccl5 14.59725 14.53721 10.59526 10.51983 8.240032
## Cd74 13.47223 13.52302 14.33849 14.12283 14.231144
## H2-Aa 12.59702 12.59058 13.09476 12.87849 12.815343df <- data.frame(genes = rep(rownames(v$E), 12),
sample = rep(colnames(v$E),each = 3000),
value = c(v$E[,1], v$E[,2], v$E[,3], v$E[,4], v$E[,5], v$E[,6], v$E[,7], v$E[,8], v$E[,9],
v$E[,10], v$E[,11], v$E[,12])
)
dfggplot(df, aes(x=sample, y=value)) +
geom_violin() +
geom_jitter(shape=16, position=position_jitter(0.2), size = 0.1)
After normalization for library size:
pca_n <- prcomp(t(v$E), center = TRUE,scale. = FALSE)
ggbiplot(pca_n, labels.size = 5, var.axes = FALSE,
choices = c(1,2),
groups = condition,
ellipse = TRUE) +
scale_color_manual(values=c("purple","green3" , "cornflowerblue", "coral1" )) +
theme(text = element_text(size=17)) 
ggbiplot(pca_n, labels.size = 5, var.axes = FALSE,
choices = c(2,3),
groups = condition,
ellipse = TRUE) +
scale_color_manual(values=c("purple","green3" , "cornflowerblue", "coral1" )) +
theme(text = element_text(size=17)) 
ggbiplot(pca_n, labels.size = 5, var.axes = FALSE,
choices = c(1,2),
groups = sorting,
ellipse = TRUE) +
scale_color_manual(values=c("cyan","blue","orange", "red")) +
theme(text = element_text(size=17)) 
ggbiplot(pca_n, labels.size = 5, var.axes = FALSE,
choices = c(2,3),
groups = sorting,
ellipse = TRUE) +
scale_color_manual(values=c("cyan","blue","orange", "red")) +
theme(text = element_text(size=17)) 
plotMDS(v$E, col = c("purple", "purple", "dodgerblue",
"dodgerblue","green3", "coral1",
"dodgerblue", "green3", "coral1",
"coral1", "dodgerblue", "green3"))
plotMDS(v$E, col = c(rep("red", 3),rep("slateblue4",2), rep( "darkgoldenrod1", 3), rep("turquoise1",4)))
# Batch effect correction - sorting
bec <- removeBatchEffect(v$E, batch = sorting)bec[1:5,1:5]## MCT1 MCT3 NT1 S1-NT S2-MCT
## Cxcl2 14.16985 14.13306 12.476182 13.27328 13.912781
## Lyz2 14.02041 13.65923 16.368329 14.65124 14.714077
## Ccl5 11.89856 11.83853 7.896577 11.68446 9.404656
## Cd74 13.68660 13.73740 14.552868 13.93813 14.046447
## H2-Aa 12.51795 12.51151 13.015692 12.71329 12.650140df2 <- data.frame(genes = rep(rownames(bec), 12),
sample = rep(colnames(bec),each = 3000),
value = c(bec[,1], bec[,2], bec[,3], bec[,4], bec[,5], bec[,6], bec[,7], bec[,8], bec[,9],
bec[,10], bec[,11], bec[,12])
)
df2ggplot(df2, aes(x=sample, y=value)) +
geom_violin() +
geom_jitter(shape=16, position=position_jitter(0.2), size = 0.1)
pca_b <- prcomp(t(bec), center = TRUE,scale. = FALSE)
ggbiplot(pca_b, labels.size = 5, var.axes = FALSE,
choices = c(1,2),
groups = condition,
ellipse = TRUE) +
scale_color_manual(values=c("purple","green3" , "cornflowerblue", "coral1" )) +
theme(text = element_text(size=17)) 
ggbiplot(pca_b, labels.size = 5, var.axes = FALSE,
choices = c(2,3),
groups = condition,
ellipse = TRUE) +
scale_color_manual(values=c("purple","green3" , "cornflowerblue", "coral1" )) +
theme(text = element_text(size=17)) 
ggbiplot(pca_b, labels.size = 5, var.axes = FALSE,
choices = c(1,2),
groups = sorting,
ellipse = TRUE) +
scale_color_manual(values=c("cyan","blue","orange", "red")) +
theme(text = element_text(size=17)) 
ggbiplot(pca_b, labels.size = 5, var.axes = FALSE,
choices = c(2,3),
groups = sorting,
ellipse = TRUE) +
scale_color_manual(values=c("cyan","blue","orange", "red")) +
theme(text = element_text(size=17)) 
plotMDS(bec, col = c("purple","purple","dodgerblue","dodgerblue","green3", "coral1",
"dodgerblue", "green3", "coral1",
"coral1", "dodgerblue", "green3"))
plotMDS(bec, col = c(rep("red", 3),rep("slateblue4",2), rep( "darkgoldenrod1", 3), rep("turquoise1",4)))
Batch effect correction - dataset
bec2 <- removeBatchEffect(v$E, batch = c("Dataset2", "Dataset2", "Dataset2",
rep("Dataset1", 9)))bec2[1:5,1:5]## MCT1 MCT3 NT1 S1-NT S2-MCT
## Cxcl2 13.64213 13.60534 11.948458 12.85129 13.49079
## Lyz2 13.23360 12.87241 15.581517 14.31242 14.37526
## Ccl5 12.83596 12.77592 8.833969 12.28113 10.00132
## Cd74 13.61336 13.66415 14.479622 13.98170 14.09001
## H2-Aa 12.53220 12.52576 13.029949 12.94331 12.88016df3 <- data.frame(genes = rep(rownames(bec2), 12),
sample = rep(colnames(bec2),each = 3000),
value = c(bec2[,1], bec2[,2], bec2[,3], bec2[,4], bec2[,5], bec2[,6], bec2[,7], bec2[,8], bec2[,9],
bec2[,10], bec2[,11], bec2[,12])
)
df3ggplot(df3, aes(x=sample, y=value)) +
geom_violin() +
geom_jitter(shape=16, position=position_jitter(0.2), size = 0.1)
pca_b2 <- prcomp(t(bec2), center = TRUE,scale. = FALSE)
ggbiplot(pca_b2, labels.size = 5, var.axes = FALSE,
choices = c(1,2),
groups = condition,
ellipse = TRUE) +
scale_color_manual(values=c("purple","green3" , "cornflowerblue", "coral1")) +
theme(text = element_text(size=17)) 
ggbiplot(pca_b2, labels.size = 5, var.axes = FALSE,
choices = c(2,3),
groups = condition,
ellipse = TRUE) +
scale_color_manual(values=c("purple","green3" , "cornflowerblue", "coral1")) +
theme(text = element_text(size=17)) 
ggbiplot(pca_b2, labels.size = 5, var.axes = FALSE,
choices = c(1,2),
groups = sorting,
ellipse = TRUE) +
scale_color_manual(values=c("cyan","blue","orange", "red")) +
theme(text = element_text(size=17)) 
ggbiplot(pca_b2, labels.size = 5, var.axes = FALSE,
choices = c(2,3),
groups = sorting,
ellipse = TRUE) +
scale_color_manual(values=c("cyan","blue","orange", "red")) +
theme(text = element_text(size=17)) 
plotMDS(bec2, col = c("purple","purple","dodgerblue","dodgerblue","green3", "coral1",
"dodgerblue", "green3", "coral1",
"coral1", "dodgerblue", "green3"))
plotMDS(bec2, col = c(rep("red", 3),rep("slateblue4",2), rep( "darkgoldenrod1", 3), rep("turquoise1",4)))
Heatmaps
annotColumn1 <- data.frame(condition = condition)
rownames(annotColumn1) <- colnames(bec) annotColumn2 <- data.frame(condition = condition,
sorting = sorting)
rownames(annotColumn2) <- colnames(bec) colors1 = list(condition = c("MCT-72h"= "green3" , NT = "cornflowerblue", "MCT-12h" = "#CD0089",
"PT" = "coral1"))colors2 = list(condition = c("MCT-72h" = "green3" , NT = "cornflowerblue", "MCT-12h" = "#CD0089", PT = "coral1" ),
sorting = c("Day1" = "slateblue4", "Day2"= "darkgoldenrod1", "Day3" = "turquoise1",
"Day4" = "red"))pheatmap(bec,
col = magma(10),
cluster_rows = TRUE,
cluster_cols = TRUE,
cutree_cols = 3,
annotation_col = annotColumn1,
annotation_colors = colors1,
scale = "row",
fontsize = 17,
show_rownames = FALSE,
row_names_side = "left",
column_title_side = "bottom",
treeheight_row = 0
#rect_gp = gpar(col = "white")
)
pheatmap(bec,
col = magma(10),
cluster_rows = TRUE,
cluster_cols = TRUE,
cutree_cols = 3,
annotation_col = annotColumn2,
annotation_colors = colors2,
scale = "row",
fontsize = 12,
show_rownames = FALSE,
row_names_side = "left",
column_title_side = "bottom",
treeheight_row = 0)
#saveRDS(bec, "/mnt/nmorais-nfs/marta/pA_karine/r_session/e12h-preliminary-analysis/pseudobulk/data/mat_bec.rds")devtools::session_info()## ─ Session info ───────────────────────────────────────────────────────────────
## setting value
## version R version 4.1.2 (2021-11-01)
## os Ubuntu 18.04.6 LTS
## system x86_64, linux-gnu
## ui X11
## language (EN)
## collate en_US.UTF-8
## ctype en_US.UTF-8
## tz Europe/Lisbon
## date 2024-07-02
## pandoc 2.11.4 @ /usr/lib/rstudio-server/bin/pandoc/ (via rmarkdown)
##
## ─ Packages ───────────────────────────────────────────────────────────────────
## package * version date (UTC) lib source
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## pheatmap * 1.0.12 2019-01-04 [1] CRAN (R 4.0.3)
## pillar 1.9.0 2023-03-22 [1] CRAN (R 4.1.2)
## pkgbuild 1.4.0 2022-11-27 [1] CRAN (R 4.1.2)
## pkgconfig 2.0.3 2019-09-22 [1] CRAN (R 4.0.0)
## pkgload 1.3.2 2022-11-16 [1] CRAN (R 4.1.2)
## plotly 4.10.1 2022-11-07 [1] CRAN (R 4.1.2)
## plyr * 1.8.9 2023-10-02 [1] CRAN (R 4.1.2)
## png 0.1-8 2022-11-29 [1] CRAN (R 4.1.2)
## polyclip 1.10-4 2022-10-20 [1] CRAN (R 4.1.2)
## prettyunits 1.1.1 2020-01-24 [1] CRAN (R 4.0.0)
## processx 3.8.0 2022-10-26 [1] CRAN (R 4.1.2)
## profvis 0.3.7 2020-11-02 [1] CRAN (R 4.0.3)
## progressr 0.13.0 2023-01-10 [1] CRAN (R 4.1.2)
## promises 1.2.1 2023-08-10 [1] CRAN (R 4.1.2)
## ps 1.7.2 2022-10-26 [1] CRAN (R 4.1.2)
## purrr 1.0.2 2023-08-10 [1] CRAN (R 4.1.2)
## R6 2.5.1 2021-08-19 [1] CRAN (R 4.1.0)
## RANN 2.6.1 2019-01-08 [1] CRAN (R 4.0.3)
## RColorBrewer 1.1-3 2022-04-03 [1] CRAN (R 4.1.2)
## Rcpp 1.0.12 2024-01-09 [1] CRAN (R 4.1.2)
## RcppAnnoy 0.0.20 2022-10-27 [1] CRAN (R 4.1.2)
## RCurl 1.98-1.10 2023-01-27 [1] CRAN (R 4.1.2)
## remotes 2.4.2.1 2023-07-18 [1] CRAN (R 4.1.2)
## reshape2 1.4.4 2020-04-09 [1] CRAN (R 4.0.2)
## reticulate 1.26 2022-08-31 [1] CRAN (R 4.1.2)
## rjson 0.2.21 2022-01-09 [1] CRAN (R 4.1.2)
## rlang 1.1.3 2024-01-10 [1] CRAN (R 4.1.2)
## rmarkdown 2.26 2024-03-05 [1] CRAN (R 4.1.2)
## ROCR 1.0-11 2020-05-02 [1] CRAN (R 4.0.3)
## rpart 4.1.19 2022-10-21 [1] CRAN (R 4.1.2)
## rstudioapi 0.15.0 2023-07-07 [1] CRAN (R 4.1.2)
## rsvd 1.0.5 2021-04-16 [1] CRAN (R 4.0.5)
## Rtsne 0.16 2022-04-17 [1] CRAN (R 4.1.2)
## S4Vectors * 0.32.4 2022-03-24 [1] Bioconductor
## sass 0.4.8 2023-12-06 [1] CRAN (R 4.1.2)
## ScaledMatrix 1.2.0 2021-10-26 [1] Bioconductor
## scales * 1.3.0 2023-11-28 [1] CRAN (R 4.1.2)
## scattermore 0.8 2022-02-14 [1] CRAN (R 4.1.2)
## sctransform 0.3.5 2022-09-21 [1] CRAN (R 4.1.2)
## sessioninfo 1.2.2 2021-12-06 [1] CRAN (R 4.1.2)
## Seurat * 4.1.1 2022-05-02 [1] CRAN (R 4.1.2)
## SeuratObject * 4.1.3 2022-11-07 [1] CRAN (R 4.1.2)
## shape 1.4.6 2021-05-19 [1] CRAN (R 4.1.0)
## shiny 1.8.0 2023-11-17 [1] CRAN (R 4.1.2)
## SingleCellExperiment * 1.16.0 2021-10-26 [1] Bioconductor
## SingleR * 1.8.1 2022-01-27 [1] Bioconductor
## sp 1.6-0 2023-01-19 [1] CRAN (R 4.1.2)
## sparseMatrixStats 1.6.0 2021-10-26 [1] Bioconductor
## spatstat.core 2.4-4 2022-05-18 [1] CRAN (R 4.1.2)
## spatstat.data 3.0-0 2022-10-21 [1] CRAN (R 4.1.2)
## spatstat.geom 3.0-6 2023-01-30 [1] CRAN (R 4.1.2)
## spatstat.random 3.1-3 2023-01-25 [1] CRAN (R 4.1.2)
## spatstat.sparse 3.0-0 2022-10-21 [1] CRAN (R 4.1.2)
## spatstat.utils 3.0-1 2022-10-19 [1] CRAN (R 4.1.2)
## stringi 1.8.3 2023-12-11 [1] CRAN (R 4.1.2)
## stringr 1.5.1 2023-11-14 [1] CRAN (R 4.1.2)
## SummarizedExperiment * 1.24.0 2021-10-26 [1] Bioconductor
## survival 3.5-0 2023-01-09 [1] CRAN (R 4.1.2)
## tensor 1.5 2012-05-05 [1] CRAN (R 4.0.3)
## tibble 3.2.1 2023-03-20 [1] CRAN (R 4.1.2)
## tidyr 1.3.1 2024-01-24 [1] CRAN (R 4.1.2)
## tidyselect 1.2.0 2022-10-10 [1] CRAN (R 4.1.2)
## urlchecker 1.0.1 2021-11-30 [1] CRAN (R 4.1.2)
## usethis 2.1.6 2022-05-25 [1] CRAN (R 4.1.2)
## utf8 1.2.4 2023-10-22 [1] CRAN (R 4.1.2)
## uwot 0.1.14 2022-08-22 [1] CRAN (R 4.1.2)
## vctrs 0.6.5 2023-12-01 [1] CRAN (R 4.1.2)
## viridis * 0.6.2 2021-10-13 [1] CRAN (R 4.1.0)
## viridisLite * 0.4.2 2023-05-02 [1] CRAN (R 4.1.2)
## withr 3.0.0 2024-01-16 [1] CRAN (R 4.1.2)
## xfun 0.42 2024-02-08 [1] CRAN (R 4.1.2)
## xtable 1.8-4 2019-04-21 [1] CRAN (R 4.0.0)
## XVector 0.34.0 2021-10-26 [1] Bioconductor
## yaml 2.3.8 2023-12-11 [1] CRAN (R 4.1.2)
## zlibbioc 1.40.0 2021-10-26 [1] Bioconductor
## zoo 1.8-12 2023-04-13 [1] CRAN (R 4.1.2)
##
## [1] /usr/local/lib/R/site-library
## [2] /usr/lib/R/site-library
## [3] /usr/lib/R/library
##
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